QSAR Analysis of Platelet‐derived Growth Inhibitors Using GA‐ANN and Shuffling Crossvalidation

Abstract

Abstract Quantitative Structure–Activity Relationship (QSAR) models for the inhibition action of some 1‐phenylbenzimidazoles on platelet‐derived growth are constructed using Genetic Algorithm and Artificial Neural Network (GA‐ANN) method. The statistical parameters of R 2 and root‐mean‐square error are 0.82 and 0.21, respectively using this method. These parameters show a considerable improvement compared to the stepwise multiple linear regression combined with ANN (stepwise MLR‐ANN). Ten‐fold shuffling crossvalidations are carried out to select the most important descriptors. Five descriptors of index of Balaban (J), average molecular weight (AMW), 3D‐Wiener index (W3D), mean atomic van der Waals volume (Sv), and total charge ( Q total ) appear in most of the models. The results of GA‐ANN are superior compared with those of GA‐MLR and GA‐PLS, which indicate that the inhibition behavior has nonlinear characteristics. The ability of GA‐ANN model in predicting inhibition behavior of 1‐phenylbenzimidazoles [log(1/IC 50 )] and its robustness is illustrated by validation techniques of leave‐one‐out and leave‐multiple‐out crossvalidations and also by Y ‐randomization technique.